You don’t see it in papers because it’s an already well-known trick to try, not so interesting for a publication, and it might work well for a certain problem/dataset and might not for another one. You can probably see similar things in application-oriented publications, where they would be trying to find the optimal results for a certain problem/dataset.

It isn’t so easy. I mean, you can make something that works more or less. But moving it to a level where it can really be useful clinically will require a huge effort. Because you need to work at extremely low false positive rates while being very sensitive at detection. And, although cancer can be quite obvious in diagnostic scans, in screening scans they are usually very subtle (because screening is done without any symptoms). It will be like looking for a needle in the haystack. And try doing this in that sensitivity/specificity requirements.

I think in general the problem with engineers working on medical topics is that they underestimate the complexity of the problems and the requirement to have in-depth insights about the problems they work on. I get a similar impression from your posts.

Because AI in medicine is something new and it is currently getting widespread gradually. Breast cancer detection is actually one of the most promising applications of AI readings in radiology. This is because it is one of the most common cancer types, which lead to screening programs in many countries; so hundreds of millions of breast scans are done annually. And actually, mammography is probably the first imaging type where we will the use of commercial AI products getting widespread.

Data leakage is something different. But here I don’t see any reason why the ml model should work any better than the scientific formula.

Here is my answer: First, let’s ask why not an exponential or logarithmic function instead of a quadratic or a higher order polynomial? Or maybe a sinusoid function? The thing is, we might be needing one of such nonlinearities, or maybe another kind of nonlinearity, based on the problem, and, we don’t know it. The idea with neural networks is that it combines many simple neurons that can learn any of these nonlinearities inside it during training. If such a quadratic relationship is a relevant feature for your problem, it will learn it, meaning that some of the neurons will end up with simulating that quadratic relationship. This is a much more flexible way than hard coding the nonlinearity right away from the beginning.